Overview

Dataset statistics

Number of variables33
Number of observations123288
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.3 MiB
Average record size in memory139.0 B

Variable types

Categorical23
DateTime1
Text1
Numeric8

Alerts

failure has constant value ""Constant
metric7_log is highly overall correlated with metric8_logHigh correlation
metric8_log is highly overall correlated with metric7_logHigh correlation
D__Z1F1 is highly imbalanced (68.0%)Imbalance
D__Z1F2 is highly imbalanced (97.9%)Imbalance
MoW_5 is highly imbalanced (65.5%)Imbalance
metric7_log is highly imbalanced (91.1%)Imbalance
metric8_log is highly imbalanced (91.1%)Imbalance
will_Fail is highly imbalanced (99.0%)Imbalance
metric2_log has 117051 (94.9%) zerosZeros
metric3_log has 114254 (92.7%) zerosZeros
metric4_log has 114167 (92.6%) zerosZeros
metric9_log has 96467 (78.2%) zerosZeros

Reproduction

Analysis started2023-06-22 08:16:12.426561
Analysis finished2023-06-22 08:16:25.579723
Duration13.15 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

failure
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
0
123288 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters123288
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 123288
100.0%

Length

2023-06-22T16:16:25.630008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:16:25.719720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 123288
100.0%

Most occurring characters

ValueCountFrequency (%)
0 123288
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 123288
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 123288
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 123288
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 123288
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 123288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 123288
100.0%

date
Date

Distinct302
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
Minimum2015-01-01 00:00:00
Maximum2015-10-30 00:00:00
2023-06-22T16:16:25.797383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:25.903146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

device
Text

Distinct1168
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
2023-06-22T16:16:26.066285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters986304
Distinct characters33
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS1F01085
2nd rowW1F0Y13C
3rd rowW1F0XKWR
4th rowW1F0X7QX
5th rowW1F0X7PR
ValueCountFrequency (%)
w1f0g9t7 302
 
0.2%
s1f0h6jg 302
 
0.2%
w1f0jy02 302
 
0.2%
s1f0kycr 302
 
0.2%
z1f0ma1s 302
 
0.2%
z1f0qlc1 302
 
0.2%
z1f0ql3n 302
 
0.2%
z1f0q8rt 302
 
0.2%
z1f0qk05 302
 
0.2%
w1f05x69 302
 
0.2%
Other values (1158) 120268
97.6%
2023-06-22T16:16:26.323129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 190276
19.3%
F 137128
13.9%
0 90220
 
9.1%
S 75496
 
7.7%
W 54409
 
5.5%
Z 39516
 
4.0%
L 24868
 
2.5%
3 23780
 
2.4%
K 18605
 
1.9%
B 17647
 
1.8%
Other values (23) 314359
31.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 587882
59.6%
Decimal Number 398422
40.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 137128
23.3%
S 75496
12.8%
W 54409
 
9.3%
Z 39516
 
6.7%
L 24868
 
4.2%
K 18605
 
3.2%
B 17647
 
3.0%
R 17531
 
3.0%
J 17359
 
3.0%
G 17125
 
2.9%
Other values (13) 168198
28.6%
Decimal Number
ValueCountFrequency (%)
1 190276
47.8%
0 90220
22.6%
3 23780
 
6.0%
6 15732
 
3.9%
5 15143
 
3.8%
2 13868
 
3.5%
4 13528
 
3.4%
7 12110
 
3.0%
9 11926
 
3.0%
8 11839
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 587882
59.6%
Common 398422
40.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 137128
23.3%
S 75496
12.8%
W 54409
 
9.3%
Z 39516
 
6.7%
L 24868
 
4.2%
K 18605
 
3.2%
B 17647
 
3.0%
R 17531
 
3.0%
J 17359
 
3.0%
G 17125
 
2.9%
Other values (13) 168198
28.6%
Common
ValueCountFrequency (%)
1 190276
47.8%
0 90220
22.6%
3 23780
 
6.0%
6 15732
 
3.9%
5 15143
 
3.8%
2 13868
 
3.5%
4 13528
 
3.4%
7 12110
 
3.0%
9 11926
 
3.0%
8 11839
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 986304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 190276
19.3%
F 137128
13.9%
0 90220
 
9.1%
S 75496
 
7.7%
W 54409
 
5.5%
Z 39516
 
4.0%
L 24868
 
2.5%
3 23780
 
2.4%
K 18605
 
1.9%
B 17647
 
1.8%
Other values (23) 314359
31.9%

metric1
Real number (ℝ)

Distinct122726
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2238857 × 108
Minimum0
Maximum2.4414048 × 108
Zeros9
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2023-06-22T16:16:26.443354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12084882
Q161297396
median1.227886 × 108
Q31.8331767 × 108
95-th percentile2.3186396 × 108
Maximum2.4414048 × 108
Range2.4414048 × 108
Interquartile range (IQR)1.2202027 × 108

Descriptive statistics

Standard deviation70460596
Coefficient of variation (CV)0.57571221
Kurtosis-1.199328
Mean1.2238857 × 108
Median Absolute Deviation (MAD)61031624
Skewness-0.01123394
Sum1.5089042 × 1013
Variance4.9646956 × 1015
MonotonicityNot monotonic
2023-06-22T16:16:26.548779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57192360 25
 
< 0.1%
165048912 25
 
< 0.1%
89196552 25
 
< 0.1%
169490248 23
 
< 0.1%
169467344 15
 
< 0.1%
89162648 15
 
< 0.1%
165040624 15
 
< 0.1%
57180136 15
 
< 0.1%
12194976 15
 
< 0.1%
165045144 13
 
< 0.1%
Other values (122716) 123102
99.8%
ValueCountFrequency (%)
0 9
< 0.1%
2048 1
 
< 0.1%
2056 2
 
< 0.1%
2168 1
 
< 0.1%
3784 1
 
< 0.1%
4224 1
 
< 0.1%
4480 1
 
< 0.1%
4560 1
 
< 0.1%
8280 1
 
< 0.1%
8616 1
 
< 0.1%
ValueCountFrequency (%)
244140480 1
< 0.1%
244138600 1
< 0.1%
244136552 1
< 0.1%
244135688 1
< 0.1%
244133240 1
< 0.1%
244132936 1
< 0.1%
244132752 1
< 0.1%
244131712 1
< 0.1%
244129416 1
< 0.1%
244127840 1
< 0.1%

metric5
Real number (ℝ)

Distinct60
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.240583
Minimum1
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2023-06-22T16:16:26.975365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q18
median10
Q312
95-th percentile58
Maximum98
Range97
Interquartile range (IQR)4

Descriptive statistics

Standard deviation15.974399
Coefficient of variation (CV)1.1217517
Kurtosis12.093188
Mean14.240583
Median Absolute Deviation (MAD)2
Skewness3.4771965
Sum1755693
Variance255.18141
MonotonicityNot monotonic
2023-06-22T16:16:27.074493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 21986
17.8%
9 13484
10.9%
11 12682
10.3%
10 11392
9.2%
7 11145
9.0%
12 9740
7.9%
6 8439
 
6.8%
13 5945
 
4.8%
14 3488
 
2.8%
5 3377
 
2.7%
Other values (50) 21610
17.5%
ValueCountFrequency (%)
1 170
 
0.1%
2 198
 
0.2%
3 803
 
0.7%
4 890
 
0.7%
5 3377
 
2.7%
6 8439
 
6.8%
7 11145
9.0%
8 21986
17.8%
9 13484
10.9%
10 11392
9.2%
ValueCountFrequency (%)
98 223
 
0.2%
95 669
0.5%
94 223
 
0.2%
92 446
0.4%
91 214
 
0.2%
90 355
0.3%
89 223
 
0.2%
78 223
 
0.2%
70 223
 
0.2%
68 446
0.4%

metric6
Real number (ℝ)

Distinct44096
Distinct (%)35.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean260054.34
Minimum8
Maximum689062
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2023-06-22T16:16:27.174558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile46
Q1221459.75
median249714
Q3310190
95-th percentile444551.4
Maximum689062
Range689054
Interquartile range (IQR)88730.25

Descriptive statistics

Standard deviation99095.255
Coefficient of variation (CV)0.38105596
Kurtosis1.9070657
Mean260054.34
Median Absolute Deviation (MAD)35154
Skewness-0.37773691
Sum3.2061579 × 1010
Variance9.8198696 × 109
MonotonicityNot monotonic
2023-06-22T16:16:27.272874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 773
 
0.6%
44 708
 
0.6%
27 633
 
0.5%
26 519
 
0.4%
29 441
 
0.4%
36 335
 
0.3%
35 288
 
0.2%
52 281
 
0.2%
45 244
 
0.2%
28 214
 
0.2%
Other values (44086) 118852
96.4%
ValueCountFrequency (%)
8 19
 
< 0.1%
9 172
0.1%
12 49
 
< 0.1%
18 31
 
< 0.1%
19 26
 
< 0.1%
20 5
 
< 0.1%
21 58
 
< 0.1%
23 71
 
0.1%
24 119
0.1%
25 184
0.1%
ValueCountFrequency (%)
689062 1
< 0.1%
689035 1
< 0.1%
688964 1
< 0.1%
688952 2
< 0.1%
687802 1
< 0.1%
687775 1
< 0.1%
687706 1
< 0.1%
687694 2
< 0.1%
684078 1
< 0.1%
684048 1
< 0.1%

DaysRunning
Real number (ℝ)

Distinct302
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105.03129
Minimum0
Maximum302
Zeros1168
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2023-06-22T16:16:27.377247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q139
median85
Q3166
95-th percentile250
Maximum302
Range302
Interquartile range (IQR)127

Descriptive statistics

Standard deviation78.062785
Coefficient of variation (CV)0.74323359
Kurtosis-0.79128456
Mean105.03129
Median Absolute Deviation (MAD)58
Skewness0.55133303
Sum12949098
Variance6093.7984
MonotonicityNot monotonic
2023-06-22T16:16:27.474005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1168
 
0.9%
1 1168
 
0.9%
2 1167
 
0.9%
3 1166
 
0.9%
4 1060
 
0.9%
5 850
 
0.7%
6 757
 
0.6%
7 757
 
0.6%
8 756
 
0.6%
9 756
 
0.6%
Other values (292) 113683
92.2%
ValueCountFrequency (%)
0 1168
0.9%
1 1168
0.9%
2 1167
0.9%
3 1166
0.9%
4 1060
0.9%
5 850
0.7%
6 757
0.6%
7 757
0.6%
8 756
0.6%
9 756
0.6%
ValueCountFrequency (%)
302 31
< 0.1%
301 31
< 0.1%
299 31
< 0.1%
298 31
< 0.1%
297 32
< 0.1%
296 32
< 0.1%
295 32
< 0.1%
294 32
< 0.1%
293 69
0.1%
292 69
0.1%

D__S1F0
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
0
90522 
1
32766 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters123288
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 90522
73.4%
1 32766
 
26.6%

Length

2023-06-22T16:16:27.567470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:16:27.649815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 90522
73.4%
1 32766
 
26.6%

Most occurring characters

ValueCountFrequency (%)
0 90522
73.4%
1 32766
 
26.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 123288
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 90522
73.4%
1 32766
 
26.6%

Most occurring scripts

ValueCountFrequency (%)
Common 123288
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 90522
73.4%
1 32766
 
26.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 123288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 90522
73.4%
1 32766
 
26.6%

D__S1F1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
0
101741 
1
21547 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters123288
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 101741
82.5%
1 21547
 
17.5%

Length

2023-06-22T16:16:27.719077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:16:27.800691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 101741
82.5%
1 21547
 
17.5%

Most occurring characters

ValueCountFrequency (%)
0 101741
82.5%
1 21547
 
17.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 123288
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 101741
82.5%
1 21547
 
17.5%

Most occurring scripts

ValueCountFrequency (%)
Common 123288
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 101741
82.5%
1 21547
 
17.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 123288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 101741
82.5%
1 21547
 
17.5%

D__W1F0
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
0
100288 
1
23000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters123288
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 100288
81.3%
1 23000
 
18.7%

Length

2023-06-22T16:16:27.869711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:16:27.951923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 100288
81.3%
1 23000
 
18.7%

Most occurring characters

ValueCountFrequency (%)
0 100288
81.3%
1 23000
 
18.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 123288
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 100288
81.3%
1 23000
 
18.7%

Most occurring scripts

ValueCountFrequency (%)
Common 123288
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 100288
81.3%
1 23000
 
18.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 123288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 100288
81.3%
1 23000
 
18.7%

D__W1F1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
0
103453 
1
19835 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters123288
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 103453
83.9%
1 19835
 
16.1%

Length

2023-06-22T16:16:28.021065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:16:28.103216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 103453
83.9%
1 19835
 
16.1%

Most occurring characters

ValueCountFrequency (%)
0 103453
83.9%
1 19835
 
16.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 123288
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 103453
83.9%
1 19835
 
16.1%

Most occurring scripts

ValueCountFrequency (%)
Common 123288
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 103453
83.9%
1 19835
 
16.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 123288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 103453
83.9%
1 19835
 
16.1%

D__Z1F0
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
0
104577 
1
18711 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters123288
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 104577
84.8%
1 18711
 
15.2%

Length

2023-06-22T16:16:28.172419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:16:28.254852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 104577
84.8%
1 18711
 
15.2%

Most occurring characters

ValueCountFrequency (%)
0 104577
84.8%
1 18711
 
15.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 123288
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 104577
84.8%
1 18711
 
15.2%

Most occurring scripts

ValueCountFrequency (%)
Common 123288
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 104577
84.8%
1 18711
 
15.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 123288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 104577
84.8%
1 18711
 
15.2%

D__Z1F1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
0
116107 
1
 
7181

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters123288
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 116107
94.2%
1 7181
 
5.8%

Length

2023-06-22T16:16:28.324419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:16:28.406204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 116107
94.2%
1 7181
 
5.8%

Most occurring characters

ValueCountFrequency (%)
0 116107
94.2%
1 7181
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 123288
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 116107
94.2%
1 7181
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
Common 123288
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 116107
94.2%
1 7181
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 123288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 116107
94.2%
1 7181
 
5.8%

D__Z1F2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
0
123040 
1
 
248

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters123288
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 123040
99.8%
1 248
 
0.2%

Length

2023-06-22T16:16:28.472916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:16:28.552776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 123040
99.8%
1 248
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 123040
99.8%
1 248
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 123288
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 123040
99.8%
1 248
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 123288
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 123040
99.8%
1 248
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 123288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 123040
99.8%
1 248
 
0.2%

DoW_0
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
0
105768 
1
17520 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters123288
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 105768
85.8%
1 17520
 
14.2%

Length

2023-06-22T16:16:28.620510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:16:28.703213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 105768
85.8%
1 17520
 
14.2%

Most occurring characters

ValueCountFrequency (%)
0 105768
85.8%
1 17520
 
14.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 123288
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 105768
85.8%
1 17520
 
14.2%

Most occurring scripts

ValueCountFrequency (%)
Common 123288
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 105768
85.8%
1 17520
 
14.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 123288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 105768
85.8%
1 17520
 
14.2%

DoW_1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
0
106062 
1
17226 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters123288
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 106062
86.0%
1 17226
 
14.0%

Length

2023-06-22T16:16:28.774283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:16:28.855697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 106062
86.0%
1 17226
 
14.0%

Most occurring characters

ValueCountFrequency (%)
0 106062
86.0%
1 17226
 
14.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 123288
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 106062
86.0%
1 17226
 
14.0%

Most occurring scripts

ValueCountFrequency (%)
Common 123288
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 106062
86.0%
1 17226
 
14.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 123288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 106062
86.0%
1 17226
 
14.0%

DoW_2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
0
106349 
1
16939 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters123288
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 106349
86.3%
1 16939
 
13.7%

Length

2023-06-22T16:16:28.925165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:16:29.005919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 106349
86.3%
1 16939
 
13.7%

Most occurring characters

ValueCountFrequency (%)
0 106349
86.3%
1 16939
 
13.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 123288
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 106349
86.3%
1 16939
 
13.7%

Most occurring scripts

ValueCountFrequency (%)
Common 123288
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 106349
86.3%
1 16939
 
13.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 123288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 106349
86.3%
1 16939
 
13.7%

DoW_3
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
0
105279 
1
18009 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters123288
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 105279
85.4%
1 18009
 
14.6%

Length

2023-06-22T16:16:29.076026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:16:29.156658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 105279
85.4%
1 18009
 
14.6%

Most occurring characters

ValueCountFrequency (%)
0 105279
85.4%
1 18009
 
14.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 123288
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 105279
85.4%
1 18009
 
14.6%

Most occurring scripts

ValueCountFrequency (%)
Common 123288
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 105279
85.4%
1 18009
 
14.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 123288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 105279
85.4%
1 18009
 
14.6%

DoW_4
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
0
105404 
1
17884 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters123288
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 105404
85.5%
1 17884
 
14.5%

Length

2023-06-22T16:16:29.226602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:16:29.308099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 105404
85.5%
1 17884
 
14.5%

Most occurring characters

ValueCountFrequency (%)
0 105404
85.5%
1 17884
 
14.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 123288
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 105404
85.5%
1 17884
 
14.5%

Most occurring scripts

ValueCountFrequency (%)
Common 123288
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 105404
85.5%
1 17884
 
14.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 123288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 105404
85.5%
1 17884
 
14.5%

DoW_5
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
0
105432 
1
17856 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters123288
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 105432
85.5%
1 17856
 
14.5%

Length

2023-06-22T16:16:29.377276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:16:29.458906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 105432
85.5%
1 17856
 
14.5%

Most occurring characters

ValueCountFrequency (%)
0 105432
85.5%
1 17856
 
14.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 123288
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 105432
85.5%
1 17856
 
14.5%

Most occurring scripts

ValueCountFrequency (%)
Common 123288
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 105432
85.5%
1 17856
 
14.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 123288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 105432
85.5%
1 17856
 
14.5%

DoW_6
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
0
105434 
1
17854 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters123288
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 105434
85.5%
1 17854
 
14.5%

Length

2023-06-22T16:16:29.526997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:16:29.608107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 105434
85.5%
1 17854
 
14.5%

Most occurring characters

ValueCountFrequency (%)
0 105434
85.5%
1 17854
 
14.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 123288
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 105434
85.5%
1 17854
 
14.5%

Most occurring scripts

ValueCountFrequency (%)
Common 123288
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 105434
85.5%
1 17854
 
14.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 123288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 105434
85.5%
1 17854
 
14.5%

MoW_1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
0
91246 
1
32042 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters123288
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 91246
74.0%
1 32042
 
26.0%

Length

2023-06-22T16:16:29.676855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:16:29.759149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 91246
74.0%
1 32042
 
26.0%

Most occurring characters

ValueCountFrequency (%)
0 91246
74.0%
1 32042
 
26.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 123288
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 91246
74.0%
1 32042
 
26.0%

Most occurring scripts

ValueCountFrequency (%)
Common 123288
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 91246
74.0%
1 32042
 
26.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 123288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 91246
74.0%
1 32042
 
26.0%

MoW_2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
0
93954 
1
29334 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters123288
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 93954
76.2%
1 29334
 
23.8%

Length

2023-06-22T16:16:29.828279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:16:29.909737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 93954
76.2%
1 29334
 
23.8%

Most occurring characters

ValueCountFrequency (%)
0 93954
76.2%
1 29334
 
23.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 123288
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 93954
76.2%
1 29334
 
23.8%

Most occurring scripts

ValueCountFrequency (%)
Common 123288
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 93954
76.2%
1 29334
 
23.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 123288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 93954
76.2%
1 29334
 
23.8%

MoW_3
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
0
95478 
1
27810 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters123288
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 95478
77.4%
1 27810
 
22.6%

Length

2023-06-22T16:16:29.977770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:16:30.059965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 95478
77.4%
1 27810
 
22.6%

Most occurring characters

ValueCountFrequency (%)
0 95478
77.4%
1 27810
 
22.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 123288
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 95478
77.4%
1 27810
 
22.6%

Most occurring scripts

ValueCountFrequency (%)
Common 123288
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 95478
77.4%
1 27810
 
22.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 123288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 95478
77.4%
1 27810
 
22.6%

MoW_4
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
0
97126 
1
26162 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters123288
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 97126
78.8%
1 26162
 
21.2%

Length

2023-06-22T16:16:30.128203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:16:30.208656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 97126
78.8%
1 26162
 
21.2%

Most occurring characters

ValueCountFrequency (%)
0 97126
78.8%
1 26162
 
21.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 123288
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 97126
78.8%
1 26162
 
21.2%

Most occurring scripts

ValueCountFrequency (%)
Common 123288
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 97126
78.8%
1 26162
 
21.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 123288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 97126
78.8%
1 26162
 
21.2%

MoW_5
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
0
115348 
1
 
7940

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters123288
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 115348
93.6%
1 7940
 
6.4%

Length

2023-06-22T16:16:30.279498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:16:30.359300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 115348
93.6%
1 7940
 
6.4%

Most occurring characters

ValueCountFrequency (%)
0 115348
93.6%
1 7940
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 123288
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 115348
93.6%
1 7940
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Common 123288
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 115348
93.6%
1 7940
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 123288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 115348
93.6%
1 7940
 
6.4%

metric2_log
Real number (ℝ)

Distinct510
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01611926
Minimum0
Maximum0.3186477
Zeros117051
Zeros (%)94.9%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2023-06-22T16:16:30.438303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.31832511
Maximum0.3186477
Range0.3186477
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.069830725
Coefficient of variation (CV)4.3321298
Kurtosis14.821133
Mean0.01611926
Median Absolute Deviation (MAD)0
Skewness4.1013281
Sum1987.3113
Variance0.0048763302
MonotonicityNot monotonic
2023-06-22T16:16:30.535719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 117051
94.9%
0.3186477013 281
 
0.2%
0.3183251136 255
 
0.2%
0.3186346333 252
 
0.2%
0.3186449345 201
 
0.2%
0.3186477013 174
 
0.1%
0.3186476995 169
 
0.1%
0.3186038644 165
 
0.1%
0.3186474583 150
 
0.1%
0.3186477005 139
 
0.1%
Other values (500) 4451
 
3.6%
ValueCountFrequency (%)
0 117051
94.9%
0.3183251136 255
 
0.2%
0.3186038644 165
 
0.1%
0.3186346333 252
 
0.2%
0.3186422334 128
 
0.1%
0.3186449345 201
 
0.2%
0.3186461199 90
 
0.1%
0.3186466613 1
 
< 0.1%
0.3186467174 102
 
0.1%
0.3186470498 26
 
< 0.1%
ValueCountFrequency (%)
0.3186477013 65
0.1%
0.3186477013 3
 
< 0.1%
0.3186477013 4
 
< 0.1%
0.3186477013 1
 
< 0.1%
0.3186477013 1
 
< 0.1%
0.3186477013 1
 
< 0.1%
0.3186477013 21
 
< 0.1%
0.3186477013 26
 
< 0.1%
0.3186477013 36
< 0.1%
0.3186477013 1
 
< 0.1%

metric3_log
Real number (ℝ)

Distinct43
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.013342768
Minimum0
Maximum0.18364369
Zeros114254
Zeros (%)92.7%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2023-06-22T16:16:30.635919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.17942897
Maximum0.18364369
Range0.18364369
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.047453853
Coefficient of variation (CV)3.5565225
Kurtosis8.7333303
Mean0.013342768
Median Absolute Deviation (MAD)0
Skewness3.2758672
Sum1645.0031
Variance0.0022518681
MonotonicityNot monotonic
2023-06-22T16:16:30.730403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0 114254
92.7%
0.1794289688 3243
 
2.6%
0.1831803588 741
 
0.6%
0.1836436933 429
 
0.3%
0.1836414733 297
 
0.2%
0.1836436926 291
 
0.2%
0.1836330596 275
 
0.2%
0.1836436843 266
 
0.2%
0.1836149951 265
 
0.2%
0.1836430347 258
 
0.2%
Other values (33) 2969
 
2.4%
ValueCountFrequency (%)
0 114254
92.7%
0.1794289688 3243
 
2.6%
0.1831803588 741
 
0.6%
0.1835469631 109
 
0.1%
0.1836149951 265
 
0.2%
0.1836330596 275
 
0.2%
0.1836414733 297
 
0.2%
0.1836425243 248
 
0.2%
0.1836430347 258
 
0.2%
0.1836433014 240
 
0.2%
ValueCountFrequency (%)
0.1836436933 429
0.3%
0.1836436933 83
 
0.1%
0.1836436933 4
 
< 0.1%
0.1836436933 1
 
< 0.1%
0.1836436933 5
 
< 0.1%
0.1836436933 4
 
< 0.1%
0.1836436933 176
0.1%
0.1836436933 2
 
< 0.1%
0.1836436933 5
 
< 0.1%
0.1836436933 83
 
0.1%

metric4_log
Real number (ℝ)

Distinct89
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.012126434
Minimum0
Maximum0.16415904
Zeros114167
Zeros (%)92.6%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2023-06-22T16:16:30.834715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.16415787
Maximum0.16415904
Range0.16415904
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.042903058
Coefficient of variation (CV)3.537978
Kurtosis8.598243
Mean0.012126434
Median Absolute Deviation (MAD)0
Skewness3.2554295
Sum1495.0438
Variance0.0018406724
MonotonicityNot monotonic
2023-06-22T16:16:30.932668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 114167
92.6%
0.164157868 3634
 
2.9%
0.1617519345 866
 
0.7%
0.1639554215 696
 
0.6%
0.1641237395 458
 
0.4%
0.1641590085 442
 
0.4%
0.1641499701 352
 
0.3%
0.164158961 290
 
0.2%
0.1641590354 244
 
0.2%
0.1641560497 229
 
0.2%
Other values (79) 1910
 
1.5%
ValueCountFrequency (%)
0 114167
92.6%
0.1617519345 866
 
0.7%
0.1639554215 696
 
0.6%
0.1641237395 458
 
0.4%
0.1641499701 352
 
0.3%
0.1641560497 229
 
0.2%
0.164157868 3634
 
2.9%
0.1641585178 163
 
0.1%
0.1641587828 164
 
0.1%
0.1641589025 39
 
< 0.1%
ValueCountFrequency (%)
0.1641590354 61
< 0.1%
0.1641590354 4
 
< 0.1%
0.1641590354 11
 
< 0.1%
0.1641590354 3
 
< 0.1%
0.1641590354 1
 
< 0.1%
0.1641590354 2
 
< 0.1%
0.1641590354 4
 
< 0.1%
0.1641590354 1
 
< 0.1%
0.1641590354 53
< 0.1%
0.1641590354 127
0.1%

metric7_log
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
0.0
121904 
0.031485000271344425
 
1384

Length

Max length20
Median length3
Mean length3.1908377
Min length3

Characters and Unicode

Total characters393392
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 121904
98.9%
0.031485000271344425 1384
 
1.1%

Length

2023-06-22T16:16:31.029633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:16:31.116479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 121904
98.9%
0.031485000271344425 1384
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 250728
63.7%
. 123288
31.3%
4 5536
 
1.4%
3 2768
 
0.7%
1 2768
 
0.7%
5 2768
 
0.7%
2 2768
 
0.7%
8 1384
 
0.4%
7 1384
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 270104
68.7%
Other Punctuation 123288
31.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 250728
92.8%
4 5536
 
2.0%
3 2768
 
1.0%
1 2768
 
1.0%
5 2768
 
1.0%
2 2768
 
1.0%
8 1384
 
0.5%
7 1384
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 123288
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 393392
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 250728
63.7%
. 123288
31.3%
4 5536
 
1.4%
3 2768
 
0.7%
1 2768
 
0.7%
5 2768
 
0.7%
2 2768
 
0.7%
8 1384
 
0.4%
7 1384
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 393392
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 250728
63.7%
. 123288
31.3%
4 5536
 
1.4%
3 2768
 
0.7%
1 2768
 
0.7%
5 2768
 
0.7%
2 2768
 
0.7%
8 1384
 
0.4%
7 1384
 
0.4%

metric8_log
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
0.0
121904 
0.031485000271344425
 
1384

Length

Max length20
Median length3
Mean length3.1908377
Min length3

Characters and Unicode

Total characters393392
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 121904
98.9%
0.031485000271344425 1384
 
1.1%

Length

2023-06-22T16:16:31.193261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:16:31.280208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 121904
98.9%
0.031485000271344425 1384
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 250728
63.7%
. 123288
31.3%
4 5536
 
1.4%
3 2768
 
0.7%
1 2768
 
0.7%
5 2768
 
0.7%
2 2768
 
0.7%
8 1384
 
0.4%
7 1384
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 270104
68.7%
Other Punctuation 123288
31.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 250728
92.8%
4 5536
 
2.0%
3 2768
 
1.0%
1 2768
 
1.0%
5 2768
 
1.0%
2 2768
 
1.0%
8 1384
 
0.5%
7 1384
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 123288
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 393392
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 250728
63.7%
. 123288
31.3%
4 5536
 
1.4%
3 2768
 
0.7%
1 2768
 
0.7%
5 2768
 
0.7%
2 2768
 
0.7%
8 1384
 
0.4%
7 1384
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 393392
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 250728
63.7%
. 123288
31.3%
4 5536
 
1.4%
3 2768
 
0.7%
1 2768
 
0.7%
5 2768
 
0.7%
2 2768
 
0.7%
8 1384
 
0.4%
7 1384
 
0.4%

metric9_log
Real number (ℝ)

Distinct66
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.093743502
Minimum0
Maximum0.48180726
Zeros96467
Zeros (%)78.2%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2023-06-22T16:16:31.359313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.47903386
Maximum0.48180726
Range0.48180726
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.17923274
Coefficient of variation (CV)1.9119484
Kurtosis0.11391256
Mean0.093743502
Median Absolute Deviation (MAD)0
Skewness1.425473
Sum11557.449
Variance0.032124376
MonotonicityNot monotonic
2023-06-22T16:16:31.461243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 96467
78.2%
0.3674983883 9334
 
7.6%
0.43253537 3679
 
3.0%
0.4546874576 2298
 
1.9%
0.4647406228 1371
 
1.1%
0.4733182619 788
 
0.6%
0.4753730825 765
 
0.6%
0.4767684848 724
 
0.6%
0.4701174951 720
 
0.6%
0.4784849275 635
 
0.5%
Other values (56) 6507
 
5.3%
ValueCountFrequency (%)
0 96467
78.2%
0.3674983883 9334
 
7.6%
0.43253537 3679
 
3.0%
0.4546874576 2298
 
1.9%
0.4647406228 1371
 
1.1%
0.4701174951 720
 
0.6%
0.4733182619 788
 
0.6%
0.4753730825 765
 
0.6%
0.4767684848 724
 
0.6%
0.477758198 331
 
0.3%
ValueCountFrequency (%)
0.481807261 4
 
< 0.1%
0.4818072593 3
 
< 0.1%
0.481807257 4
 
< 0.1%
0.4818072278 5
 
< 0.1%
0.4818072235 83
0.1%
0.4818072198 178
0.1%
0.4818072096 4
 
< 0.1%
0.4818072095 1
 
< 0.1%
0.4818071832 4
 
< 0.1%
0.4818070538 117
0.1%

will_Fail
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
0
123182 
1
 
106

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters123288
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 123182
99.9%
1 106
 
0.1%

Length

2023-06-22T16:16:31.549725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:16:31.631434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 123182
99.9%
1 106
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 123182
99.9%
1 106
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 123288
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 123182
99.9%
1 106
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 123288
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 123182
99.9%
1 106
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 123288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 123182
99.9%
1 106
 
0.1%

Interactions

2023-06-22T16:16:23.889641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:18.906833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:19.623783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:20.315019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:21.035369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:21.731626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:22.437225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:23.186318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:23.973356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:19.004786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:19.708397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:20.403642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:21.120658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:21.817650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:22.525748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:23.270328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:24.055796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:19.088599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:19.790883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:20.491020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:21.204861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:21.903697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:22.623876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:23.356102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:24.144836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:19.181035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:19.880347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:20.581271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:21.293516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:21.993623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:22.732800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:23.446129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:24.232167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:19.268767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:19.967257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:20.671287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:21.378764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:22.082487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:22.824245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:23.534124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:24.318553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:19.356810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:20.053328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:20.761281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:21.465633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:22.170164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:22.914841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:23.622141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:24.409678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:19.449667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:20.144208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:20.856166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:21.557952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:22.262619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:23.009546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:23.716099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:24.495837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:19.538315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:20.229744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:20.946782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:21.645079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:22.350054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:23.098203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:16:23.802500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-22T16:16:31.713917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
metric1metric5metric6DaysRunningmetric2_logmetric3_logmetric4_logmetric9_logD__S1F0D__S1F1D__W1F0D__W1F1D__Z1F0D__Z1F1D__Z1F2DoW_0DoW_1DoW_2DoW_3DoW_4DoW_5DoW_6MoW_1MoW_2MoW_3MoW_4MoW_5metric7_logmetric8_logwill_Fail
metric11.000-0.005-0.002-0.005-0.0010.0030.001-0.0030.0030.0040.0000.0050.0040.0040.0000.0060.0040.0000.0090.0040.0030.0090.0000.0000.0000.0000.0020.0000.0000.000
metric5-0.0051.0000.085-0.014-0.0250.109-0.0200.0350.1270.1800.1890.1340.2090.1180.0370.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0080.0070.0280.0280.007
metric6-0.0020.0851.0000.185-0.0790.0700.0110.0900.1870.2210.1810.4130.3040.2060.1070.0000.0080.0140.0090.0000.0000.0000.0830.0270.0270.0330.0340.0980.0980.013
DaysRunning-0.005-0.0140.1851.000-0.0200.003-0.019-0.0190.0640.0520.0680.0360.0320.0440.0410.0430.0510.0480.0450.0460.0400.0400.0940.0420.0360.0650.1300.0710.0710.013
metric2_log-0.001-0.025-0.079-0.0201.000-0.0200.221-0.0310.0100.0420.0160.0940.0500.0040.0100.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0040.0040.1010.1010.052
metric3_log0.0030.1090.0700.003-0.0201.0000.1210.3910.0000.1030.0630.0160.0010.0070.1020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0090.0090.001
metric4_log0.001-0.0200.011-0.0190.2210.1211.0000.0480.0560.1090.0880.0280.0430.0380.0120.0000.0030.0030.0000.0000.0000.0000.0170.0050.0030.0050.0020.1540.1540.049
metric9_log-0.0030.0350.090-0.019-0.0310.3910.0481.0000.0840.1090.0850.0860.0430.0480.0230.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0220.0220.004
D__S1F00.0030.1270.1870.0640.0100.0000.0560.0841.0000.2770.2880.2630.2540.1500.0270.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0080.0270.0270.004
D__S1F10.0040.1800.2210.0520.0420.1030.1090.1090.2771.0000.2200.2010.1950.1140.0200.0000.0000.0020.0000.0000.0000.0000.0120.0030.0070.0000.0000.0450.0450.008
D__W1F00.0000.1890.1810.0680.0160.0630.0880.0850.2880.2201.0000.2100.2030.1190.0210.0000.0000.0010.0000.0000.0000.0000.0140.0030.0060.0000.0000.0350.0350.004
D__W1F10.0050.1340.4130.0360.0940.0160.0280.0860.2630.2010.2101.0000.1850.1090.0190.0000.0000.0010.0000.0000.0000.0000.0100.0020.0040.0000.0000.0120.0120.000
D__Z1F00.0040.2090.3040.0320.0500.0010.0430.0430.2540.1950.2030.1851.0000.1050.0190.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0030.0060.0300.0300.000
D__Z1F10.0040.1180.2060.0440.0040.0070.0380.0480.1500.1140.1190.1090.1051.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0290.0290.000
D__Z1F20.0000.0370.1070.0410.0100.1020.0120.0230.0270.0200.0210.0190.0190.0101.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0030.000
DoW_00.0060.0000.0000.0430.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.1640.1620.1680.1680.1670.1670.0000.0000.0000.0040.0110.0000.0000.000
DoW_10.0040.0000.0080.0510.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.1641.0000.1610.1670.1660.1660.1660.0060.0020.0010.0080.0120.0000.0000.000
DoW_20.0000.0000.0140.0480.0000.0000.0030.0000.0000.0020.0010.0010.0000.0000.0000.1620.1611.0000.1650.1640.1640.1640.0050.0030.0090.0060.0240.0000.0000.003
DoW_30.0090.0000.0090.0450.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1680.1670.1651.0000.1700.1700.1700.0000.0050.0050.0050.0250.0000.0000.000
DoW_40.0040.0000.0000.0460.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1680.1660.1640.1701.0000.1690.1690.0010.0020.0040.0090.0250.0000.0000.005
DoW_50.0030.0000.0000.0400.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1670.1660.1640.1700.1691.0000.1690.0030.0000.0000.0070.0080.0000.0000.008
DoW_60.0090.0000.0000.0400.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1670.1660.1640.1700.1690.1691.0000.0020.0000.0000.0020.0120.0020.0020.008
MoW_10.0000.0100.0830.0940.0080.0000.0170.0030.0080.0120.0140.0100.0000.0000.0000.0000.0060.0050.0000.0010.0030.0021.0000.3310.3200.3080.1550.0030.0030.000
MoW_20.0000.0000.0270.0420.0000.0000.0050.0000.0000.0030.0030.0020.0000.0000.0000.0000.0020.0030.0050.0020.0000.0000.3311.0000.3020.2900.1470.0040.0040.000
MoW_30.0000.0000.0270.0360.0000.0000.0030.0000.0000.0070.0060.0040.0020.0000.0000.0000.0010.0090.0050.0040.0000.0000.3200.3021.0000.2800.1420.0000.0000.006
MoW_40.0000.0080.0330.0650.0040.0000.0050.0000.0000.0000.0000.0000.0030.0000.0000.0040.0080.0060.0050.0090.0070.0020.3080.2900.2801.0000.1360.0000.0000.000
MoW_50.0020.0070.0340.1300.0040.0000.0020.0000.0080.0000.0000.0000.0060.0000.0000.0110.0120.0240.0250.0250.0080.0120.1550.1470.1420.1361.0000.0000.0000.000
metric7_log0.0000.0280.0980.0710.1010.0090.1540.0220.0270.0450.0350.0120.0300.0290.0030.0000.0000.0000.0000.0000.0000.0020.0030.0040.0000.0000.0001.0001.0000.093
metric8_log0.0000.0280.0980.0710.1010.0090.1540.0220.0270.0450.0350.0120.0300.0290.0030.0000.0000.0000.0000.0000.0000.0020.0030.0040.0000.0000.0001.0001.0000.093
will_Fail0.0000.0070.0130.0130.0520.0010.0490.0040.0040.0080.0040.0000.0000.0000.0000.0000.0000.0030.0000.0050.0080.0080.0000.0000.0060.0000.0000.0930.0931.000

Missing values

2023-06-22T16:16:24.659236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-22T16:16:25.166757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

failuredatedevicemetric1metric5metric6DaysRunningD__S1F0D__S1F1D__W1F0D__W1F1D__Z1F0D__Z1F1D__Z1F2DoW_0DoW_1DoW_2DoW_3DoW_4DoW_5DoW_6MoW_1MoW_2MoW_3MoW_4MoW_5metric2_logmetric3_logmetric4_logmetric7_logmetric8_logmetric9_logwill_Fail
002015-01-01S1F010852156306726407438010000000001000100000.3186470.00.1641590.00.00.4753730
102015-01-01W1F0Y13C2343186404185772000100000001000100000.0000000.00.0000000.00.00.4546870
202015-01-01W1F0XKWR89660704730000100000001000100000.0000000.00.0000000.00.00.0000000
302015-01-01W1F0X7QX16201345612217686000100000001000100000.0000000.00.0000000.00.00.0000000
402015-01-01W1F0X7PR131383929191343000100000001000100000.0000000.00.0000000.00.00.0000000
502015-01-01W1F0X7P23322483213216960000100000001000100000.0000000.00.0000000.00.00.0000000
602015-01-01W1F0X7111853863215217590000100000001000100000.0000000.00.0000000.00.00.0000000
702015-01-01W1F0Y2PY1320330805182050000100000001000100000.0000000.00.0000000.00.00.0000000
802015-01-01W1F0X70N16558355213186288000100000001000100000.0000000.00.0000000.00.00.0000000
902015-01-01W1F0X6V017503132011213515000100000001000100000.0000000.00.0000000.00.00.0000000
failuredatedevicemetric1metric5metric6DaysRunningD__S1F0D__S1F1D__W1F0D__W1F1D__Z1F0D__Z1F1D__Z1F2DoW_0DoW_1DoW_2DoW_3DoW_4DoW_5DoW_6MoW_1MoW_2MoW_3MoW_4MoW_5metric2_logmetric3_logmetric4_logmetric7_logmetric8_logmetric9_logwill_Fail
12442102015-10-30W1F0FY9270196481535361630200100000000100000010.0000000.1794290.1641580.00.00.3674980
12442202015-10-30S1F0GGPP741166161235956730210000000000100000010.0000000.1836440.0000000.00.00.0000000
12442302015-10-30S1F0GCED1369039121133947130210000000000100000010.3186480.0000000.1641590.00.00.0000000
12442402015-10-30S1F0H6JG1450107201034385430210000000000100000010.0000000.0000000.0000000.00.00.0000000
12442502015-10-30S1F0KYCR16050721235174730210000000000100000010.0000000.0000000.0000000.00.00.0000000
12442602015-10-30S1F0S561154205281634960430210000000000100000010.0000000.0000000.0000000.00.00.0000000
12442702015-10-30S1F10HH51747302961248137630201000000000100000010.0000000.0000000.0000000.00.00.0000000
12442802015-10-30S1F10RWZ517827761535766630201000000000100000010.0000000.0000000.0000000.00.00.0000000
12442902015-10-30W1F05X6979238024534882330200100000000100000010.0000000.0000000.1639550.00.00.4647410
12443002015-10-30S1F0GPXY23511921135014530210000000000100000010.0000000.0000000.0000000.00.00.4784850